Best Practices For Data Quality In Salesforce
For achieving success with SFDC, there is a need
for an effective plan when it comes to entering, cleaning and updating of the
data for salesforce.com system. The most popular reasons for CRM system
failures point towards poor planning and management of data entry or data
quality issue. The data quality challenges faced by the SFDC customers, the
respective solutions for those challenges and the best practices will differ
depending up on the sales process and size of the company. The implementation
of the best practices for data management is vital in creating a competitive
advantage and profit with the SFDC investment being made.
How important is Data
Quality among Salesforce.com customers?
SFDC has tremendous impact in the sales and
marketing functions of a company similar to the services and support functions
within a company. Industry analysts have claimed the top most reasons that
determine the success or failure of CRM projects is the data quality.
Dynamic Process - Most of the companies across the globe are
looking forward for ways that would help them manage sales knowledge delivery
in a quicker and efficient manner. The companies feel that they are not able to
keep up with the changing market trends even by posting monthly, quarterly and
annual information.
Quick Access - There is also a greater need for improvement
when it comes to accessing information. The information pulled from several
sources needs to be aggregated in a single place in such a way that the linking
would be transparent to the sales team member.
Enhanced distribution of
knowledge - By means of setting up
individual preferences through a user-defined profile, a mechanism can be
created for updating the sales people about any major developments in the sales
and marketing segment whenever made available to the users.
CRM success driven by
end user satisfaction
The sales people were hesitant to make use of
CRM applications in the 1990 because of its complex and time consuming
functionalities. Extensive research and manual data entry were required to
understand the leads, accounts, opportunities and contacts in to the CRM
application. All these developments made end user to lose confidence in the CRM
applications. However with the introduction of Salesforce, the end users were
provided with accurate data with reduced effort and thereby resulting in
enhanced end user satisfaction.
Data Quality Parameters
- Record completeness
- Preciseness of data elements
- The overall count of duplicate records
- Determining value based related data elements from the
account record
Data quality issues, challenges
and best practices
SFDC provides scalable CRM solutions for small,
medium and large SFDC customers through varied data management best practices.
Best practices for small
SFDC customers
- Include the present and complete account records at the
time of initial SFDC implementation.
- Create accurate account records that are complete and
unique by means of licensing deduplication software tools that would help
stay away from migrating redundant records in to SFDC, especially when the
account data is coming from multiple sources.
- Automating the viewing and saving functionality using
one mouse click where in the users would be able to save lead, account and
contact data directly in to SFDC.
Best practices for
medium SFDC customers
- Implementation of a data standard for account naming
- Removing the presence of duplicate records using an
automated process and thereby helping SFDC customer to manage record
redundancy issue for huge records volumes
Best practices for large
SFDC customers
- The common attributes by the customers on areas where
the Salesforce related to the products or services is gaining momentum has
to be taken in to consideration.
- Those attributes can be then used for determining the
goal-oriented market to focus up on and the prospects of that specific
products or services towards which customers has shown interest.
- Attributes that defines the prospects usually includes
size, industry and corporate linkages etc.
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